AI and Machine Learning models can help you ADAPT to extraordinary situations like Covid

Some experts opine that AI and Machine Learning models are defunct in the age of COVID. All of them need historical data; since COVID has altered history significantly, AI models built on the last 3-4 years history are not useful. We respectfully disagree. With a little skill, AI can help you navigate and adapt to extraordinary situations like COVID.

There is a growing chorus among critics of AI & Machine Learning that the moment of reckoning for the AI community is here. While the specifics differ, the common theme in the criticism follows this argument: AI & ML models rely on the past for predicting pretty much everything – demand forecast, customer segmentation, price optimization and supply chain & logistic optimization. Now that the Corona pandemic has affected both the supply and the demand sides of the market, these AI & ML models have become irrelevant. Some go even further to suggest these approaches are now as good as useless.

While the concern itself is relevant, the conclusions inferred are at best from a place of cynicism or worst from naive understanding of AI & ML models. First a disclaimer: Any individual who claims AI & ML models would be able to predict with any reasonable accuracy the impact of such once-in-a-century event on supply & demand is doing a disservice to AI & ML community.

Now to address the criticism. Yes, these models rely on historical precedents to infer impact that are statistically sound. For example, the impact of promotions and annual events like Easter, Memorial Day, Christmas, Diwali, Ramzan etc are based on historical data and the current prevailing market conditions. However, to claim the models are now irrelevant is incorrect. There are evolving markets conditions where these models would still be useful. Three immediate use cases are highlighted

Consider assessing the impact of supply side constraints. In the initial stages of the pandemic, for various industries, for example electronics, the impact was largely on the supply side. AI & ML models would provide the right tools to assess the impact of these constraints by imputing product substitutions that can happen in such scenarios.

Once the pandemic spread out, there was a clear uplift or drop in sales depending on the market verticals. Grocery sales picked up, Electronics and Fashion went down. As mentioned earlier, while the AI & ML models would fail to assess the size of this uplift or drop, they can however be fine-tuned to quickly account for this event. As the conditions evolve, the models can be trained to give precedence to more recent data to better assess customer preferences as long as the pandemic affect prevails.

These models can still be used post pandemic. There are fairly well-known methods that allow AI & ML models to treat these dip/uplift as events. The claim that all historical data is now obsolete/irrelevant is grossly incorrect. For example, if a store observes an unprecedented demand during an annual event for any reason, it does not mean all historical data becomes irrelevant. All it points to would be the need to carefully model such outlier events and then the models would be good to go.

Summary – Careful use of historical data, especially the few weeks before the onset of the COVID pandemic, can help businesses adapt better to the situation